Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	30,124
2	Florida	27,192
3	Arizona	26,868
4	Mississippi	25,050
5	Alabama	22,735
6	New York	22,202
7	New Jersey	21,419
8	Georgia	21,403
9	South Carolina	21,055
10	Nevada	20,494
11	Texas	20,093
12	Tennessee	19,740
13	Rhode Island	19,629
14	District of Columbia	18,921
15	Massachusetts	18,050
16	Arkansas	17,965
17	Delaware	17,091
18	Iowa	16,969
19	Maryland	16,907
20	Illinois	16,879
21	California	16,368
22	Idaho	16,132
23	Nebraska	16,046
24	Utah	14,862
25	Connecticut	14,392
26	North Carolina	14,158
27	Virginia	12,772
28	Oklahoma	12,616
29	Indiana	12,523
30	Kansas	12,471
31	Wisconsin	12,400
32	South Dakota	11,943
33	Minnesota	11,820
34	North Dakota	11,773
35	Missouri	11,688
36	New Mexico	11,326
37	Michigan	10,437
38	Pennsylvania	10,228
39	Kentucky	9,615
40	Ohio	9,485
41	Washington	9,401
42	Colorado	9,367
43	Puerto Rico	8,746
44	Alaska	7,169
45	Wyoming	5,926
46	Oregon	5,663
47	Montana	5,591
48	New Hampshire	5,174
49	West Virginia	4,910
50	Hawaii	3,945
51	Maine	3,149
52	Vermont	2,456

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Mississippi	270
2	Georgia	222
3	Texas	220
4	Kansas	210
5	Alabama	207
6	Tennessee	204
7	Puerto Rico	201
8	Idaho	193
9	Nevada	189
10	Missouri	178
11	Arkansas	171
12	North Dakota	166
13	Florida	164
14	Kentucky	154
15	California	153
16	Illinois	153
17	Rhode Island	144
18	Louisiana	143
19	Hawaii	133
20	Oklahoma	133
21	South Carolina	123
22	Iowa	115
23	Nebraska	115
24	Wisconsin	112
25	South Dakota	110
26	North Carolina	107
27	Utah	103
28	Indiana	99
29	Arizona	92
30	Virginia	91
31	Minnesota	86
32	Alaska	85
33	Wyoming	82
34	Maryland	79
35	Washington	74
36	Ohio	73
37	Montana	70
38	Delaware	65
39	District of Columbia	63
40	Michigan	63
41	New Mexico	54
42	Oregon	49
43	Pennsylvania	48
44	West Virginia	44
45	Colorado	43
46	Massachusetts	39
47	Connecticut	38
48	New Jersey	36
49	New York	29
50	Maine	16
51	New Hampshire	11
52	Vermont	9

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,793
2	New York	1,668
3	Massachusetts	1,287
4	Connecticut	1,250
5	Louisiana	991
6	Rhode Island	969
7	District of Columbia	850
8	Mississippi	726
9	Michigan	662
10	Arizona	637
11	Illinois	634
12	Delaware	611
13	Maryland	605
14	Pennsylvania	593
15	Indiana	472
16	Florida	462
17	South Carolina	458
18	Georgia	446
19	Alabama	396
20	Texas	380
21	Nevada	368
22	New Mexico	347
23	Ohio	334
24	Colorado	330
25	Iowa	320
26	Minnesota	316
27	New Hampshire	314
28	California	295
29	Virginia	282
30	Washington	250
31	Missouri	242
32	North Carolina	234
33	Tennessee	210
34	Arkansas	209
35	Kentucky	194
36	Nebraska	192
37	Wisconsin	183
38	Oklahoma	176
39	North Dakota	175
40	South Dakota	175
41	Idaho	163
42	Kansas	144
43	Utah	118
44	Puerto Rico	111
45	Oregon	96
46	Maine	94
47	Vermont	92
48	West Virginia	92
49	Montana	78
50	Wyoming	58
51	Alaska	36
52	Hawaii	28

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Mississippi	8
2	Florida	7
3	Louisiana	7
4	Arizona	6
5	Nevada	6
6	Texas	6
7	South Carolina	5
8	Georgia	4
9	Idaho	4
10	Tennessee	4
11	Alabama	3
12	Arkansas	3
13	California	3
14	Iowa	3
15	Oklahoma	3
16	Indiana	2
17	Kentucky	2
18	Missouri	2
19	New Mexico	2
20	North Carolina	2
21	North Dakota	2
22	Ohio	2
23	Puerto Rico	2
24	Washington	2
25	Wyoming	2
26	District of Columbia	1
27	Illinois	1
28	Kansas	1
29	Maryland	1
30	Massachusetts	1
31	Minnesota	1
32	Nebraska	1
33	Oregon	1
34	Pennsylvania	1
35	Rhode Island	1
36	Utah	1
37	Virginia	1
38	West Virginia	1
39	Wisconsin	1
40	Alaska	0
41	Colorado	0
42	Connecticut	0
43	Delaware	0
44	Hawaii	0
45	Maine	0
46	Michigan	0
47	Montana	0
48	New Hampshire	0
49	New Jersey	0
50	New York	0
51	South Dakota	0
52	Vermont	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	140,553	1	99
Lafayette	Florida	133,223	2	99
Lake	Tennessee	115,023	3	99
Lee	Arkansas	110,986	4	99
Lincoln	Arkansas	104,115	5	99
Davidson	Tennessee	35,578	146	95
Richland	South Carolina	23,004	405	87
Orange	California	14,015	928	70
York	South Carolina	13,755	963	69
Pierce	Washington	7,649	1706	45

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	4,375	1	99
Galax city	Virginia	4,096	2	99
Randolph	Georgia	3,836	3	99
Terrell	Georgia	3,517	4	99
Neshoba	Mississippi	3,331	5	99
Richland	South Carolina	428	660	78
Davidson	Tennessee	339	839	73
Orange	California	262	1040	66
Pierce	Washington	182	1328	57
York	South Carolina	114	1677	46

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.